1. Field of the Invention
The present invention relates to detection of seizures, and more particularly, to detection of seizures with advanced algorithms.
2. Background Art
Epilepsy is a condition characterized by recurrent seizures which are the outward manifestation of excessive or hypersynchronous abnormal electrical activity of neurons in the cerebral cortex of the brain. A seizure patient may suffer from several different types of seizures, or any combination thereof. For example, a common type of epilepsy is called the grand mal seizure, which is manifested by symptoms of convulsions with tonic-clonic contractions of muscles. Another type of epilepsy is called the absence seizure, which is characterized by brief and sudden loss of consciousness. Other types of seizures include complex partial seizure, which is characterized by a complete loss of consciousness, and psychomotor seizure, which is characterized by clouding of consciousness for one to two minutes. Some types of seizures may involve the entire brain, while other types of seizures may affect only a local portion of the brain.
Electroencephalograms (EEGs) have been employed to record electrical signals generated by different parts of the brain. In a typical EEG, a plurality of electrodes are placed across the scalp of a seizure patient with predetermined spacings. FIG. 1 shows a diagram illustrating a typical arrangement of electrodes positioned on the scalp of an epilepsy patient along standard lines of measurements. Voltages measured across predetermined pairs of electrodes, for example, C3-F3, F3-F7, F7-T3, T3-T5, etc., are measured simultaneously and recorded as waveforms over a long period of time. Such simultaneous recording of voltage waveforms across different pairs of electrodes is commonly referred to as a montage, a typical example of which is illustrated in FIG. 2. The voltage waveform across a given pair of electrodes in the montage of an EEG recording is commonly referred to as a channel. A seizure is typically manifested by a highly rhythmic pattern of voltage waveforms on an EEG recording. However, depending upon the individual patient, different types of seizures, and various other factors, an onset of seizure is sometimes not readily discernable by a human reader from a montage of an EEG recording. For example, sometimes a seizure may manifest itself as a random waveform pattern across a montage of an EEG recording. Sometimes recording errors may occur in one or more channels of a montage of an EEG recording. Sometimes an onset of seizure is not shown on an EEG recording as a rhythmic pattern of waveforms, but rather as an abnormal change from the background waveform pattern.
A human reader of an EEG recording may need to go through hours or even days of recorded waveforms to determine the onset, duration, and type of seizures that may have occurred during that time. The human reader may miss an occurrence of a seizure, which is referred to as a false negative, or may mark a segment of the waveforms as a seizure event, which is referred to as a false positive.
Conventional algorithms have been developed to assist a human reader in detecting seizures using traditional fast Fourier transform (FFT) or other spectral analysis techniques. While these traditional techniques are usually effective in detecting highly rhythmic patterns of waveforms in order to identify a seizure event, some types of seizures which are not manifested by such highly rhythmic patterns may still be missed. Signal processing using conventional spectral analysis techniques may also sometimes return false positives, depending upon the perimeters set by the algorithm.
Therefore, there is a need for an improved system and method for detecting seizures from EEG recordings with a high degree of reliability.
The present invention provides a method of detecting a seizure, generally comprising the steps of:
(a) dividing a digitized waveform of an electroencephalogram (EEG) recording into a plurality of epochs each having a first predetermined duration;
(b) computing matching pursuit for a given one of the epochs to obtain a plurality of seizure atoms;
(c) describing the seizure atoms and the given epoch with at least one neural network (NN) rule;
(d) applying connected-object clustering to the epochs in a sliding window of a second predetermined duration to obtain a clustering result; and
(e) establishing a seizure point from the clustering result.
Advantageously, the method in an embodiment according to the present invention which utilizes advanced numerical analysis techniques including matching pursuit, neural network rules and connected-object clustering is capable of improving seizure detection with a high degree of accuracy and reliability.